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Vetted Predictive Modeling Professionals
Pre-screened and vetted.
Hemanth Sai Chandika
Mid-level Marketing & Product Growth Analyst specializing in FinTech and FMCG analytics
“CRM and lifecycle marketer who evolved from loyalty data segmentation at PepsiCo to owning end-to-end lifecycle strategy at Affirm. Stands out for combining HubSpot execution, attribution and CLV modeling, and data infrastructure improvements to drive a reported 29% gain in acquisition and retention efficiency within an eCommerce checkout ecosystem.”
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level Data Scientist specializing in ML, MLOps, and forecasting for FinTech and AI hardware
Junior AI Research Engineer specializing in NLP, speech and generative AI
Junior Software Engineer specializing in full-stack and cloud-native systems
Intern AI/Data Science Engineer specializing in LLM agents, data engineering, and predictive analytics
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG for financial services
Mid-level Software Engineer specializing in cloud-native microservices and ML-driven automation
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Senior Data Engineer specializing in Cloud Data Platforms and Generative AI
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
Johnnie Sanders
Executive AI Architect specializing in enterprise cloud and FinTech solutions
“Candidate brings an operator-to-founder profile with leadership experience in IT and Business Systems and a strong grasp of how ideas become venture-backable products. They speak fluently about startup evaluation criteria such as TAM, technical defensibility, speed to scale, and AI differentiation, and appear especially motivated by building solutions end-to-end in startup or venture studio environments.”
Kshitij Singh
Intern Software Developer specializing in healthcare data and systems analysis
“Candidate comes from SaaS and healthcare analytics rather than game development, but has strong end-to-end ownership experience building real-time, high-availability systems in Python/AWS. They highlight measurable impact across performance, throughput, uptime, and cost reduction, including queue optimization and predictive ICU utilization pipelines, and are looking to transfer that systems engineering foundation into Unity/gameplay work.”
NEHA KOLAN
Mid-Level Software Engineer specializing in microservices and cloud data pipelines
“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”
“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”
“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”
Sasi Katamneni
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”